Information processing system and method for a flow cytometer and flow cytometer

Abstract

The application discloses an information processing method and system for a flow cytometer and the flow cytometer. The information processing method comprises: detecting a target sample to obtain a plurality of target detection signals; calculating an adjustment matrix; and adjusting the plurality of target detection signals based on the adjustment matrix to obtain adjusted target detection signals. The adjustment matrix is calculated by: dividing the plurality of target detection signals into a first subset and a second subset based on first components and second components of the plurality of target detection signals corresponding to a first detector and a second detector of two different detectors in the plurality of detectors; and obtaining a first element of first elements and second elements of the adjustment matrix corresponding to the two different detectors based on the first components and the second components of each target detection signal in the first subset, and obtaining the second element based on the first components and the second components of each target detection signal in the second subset.

Description

Technical Field

[0001] This disclosure relates to the field of flow cytometry, and more specifically to information processing systems and methods for flow cytometry and flow cytometry instruments. Background Technology

[0002] In traditional flow cytometry, a compensation process (also known as a "demixing process") is required to eliminate signal interference between different detection channels to obtain the final detection signal used to determine the label abundance of each unit (e.g., cells, particles, etc.) in the sample. However, the compensation matrix obtained from a single-stained sample is often imperfect, especially in the case of high-parameter panels. Therefore, further adjustments are needed to the detection signal after compensation based on the compensation matrix. Even in spectroscopic flow cytometry, similar adjustments are sometimes required. Summary of the Invention

[0003] A brief overview of this disclosure is given below to provide a basic understanding of certain aspects of it. However, it should be understood that this overview is not an exhaustive summary of this disclosure. It is not intended to identify key or essential parts of this disclosure, nor is it intended to limit the scope of this disclosure. Its purpose is merely to present certain concepts of this disclosure in a simplified form as a prelude to the more detailed description that follows.

[0004] The purpose of this disclosure is to provide an improved information processing system and method for flow cytometers, as well as a flow cytometer.

[0005] According to one aspect of this disclosure, an information processing method for a flow cytometer is provided, comprising: detecting a target sample using the flow cytometer to obtain a plurality of target detection signals; calculating an adjustment matrix; and adjusting the plurality of target detection signals based on the adjustment matrix to obtain adjusted target detection signals. The adjustment matrix is ​​calculated as follows: dividing the plurality of target detection signals into a first subset and a second subset based on a first component corresponding to a first detector among two different detectors included in the flow cytometer and a second component corresponding to a second detector among the two different detectors; obtaining a first element of the adjustment matrix corresponding to the two different detectors based on the first and second components of each target detection signal in the first subset; and obtaining a second element of the adjustment matrix corresponding to the two different detectors based on the first and second components of each target detection signal in the second subset.

[0006] According to another aspect of this disclosure, an information processing system for a flow cytometer is provided, including a detection signal acquisition unit, an adjustment matrix calculation unit, and a detection signal adjustment unit. The detection signal acquisition unit can be configured to detect a target sample using the flow cytometer to obtain multiple target detection signals. The adjustment matrix calculation unit can be configured to calculate an adjustment matrix as follows: based on a first component of the multiple target detection signals corresponding to a first detector among two different detectors included in the flow cytometer, and a second component corresponding to a second detector among the two different detectors, the multiple target detection signals are divided into a first subset and a second subset; based on the first and second components of each target detection signal in the first subset, a first element of the two elements in the adjustment matrix corresponding to the two different detectors is obtained; and based on the first and second components of each target detection signal in the second subset, a second element of the two elements in the adjustment matrix corresponding to the two different detectors is obtained. The detection signal adjustment unit can be configured to adjust the multiple target detection signals based on the adjustment matrix to obtain adjusted target detection signals.

[0007] According to another aspect of this disclosure, a flow cytometer including the above-described information processing system is provided.

[0008] In accordance with other aspects of this disclosure, computer program code and computer program products for implementing the methods according to this disclosure are also provided, as well as a computer-readable storage medium having the computer program code for implementing the methods according to this disclosure recorded thereon.

[0009] Other aspects of embodiments of this disclosure are set forth in the following description section, wherein preferred embodiments of the present disclosure are described in detail without limiting them. Attached Figure Description

[0010] This disclosure can be better understood by referring to the detailed description given below in conjunction with the accompanying drawings, in which the same or similar reference numerals are used throughout the drawings to denote the same or similar parts. These drawings, together with the following detailed description, are incorporated in and form part of this specification, and are used to further illustrate preferred embodiments of the disclosure and explain the principles and advantages of the disclosure. Wherein:

[0011] Figure 1 This is a flowchart illustrating an example of an information processing method for a flow cytometer according to an embodiment of the present disclosure;

[0012] Figure 2This is an example showing a logarithmic scatter plot drawn based on two signal components of multiple target detection signals;

[0013] Figure 3 This is an example showing a linear scatter plot drawn based on two signal components of multiple target detection signals;

[0014] Figure 4 This is an example showing a logarithmic scatter plot drawn based on two signal components of adjusted multiple target detection signals;

[0015] Figure 5 This is a block diagram illustrating an example configuration of an information processing system for a flow cytometer according to embodiments of the present disclosure; and

[0016] Figure 6 This is a block diagram illustrating an example structure of a personal computer that may be employed as an embodiment of this disclosure. Detailed Implementation

[0017] Exemplary embodiments of the present disclosure will be described below with reference to the accompanying drawings. For clarity and brevity, not all features of actual implementations are described in the specification. However, it should be understood that many implementation-specific decisions must be made in the development of any such actual embodiment to achieve the developer’s specific goals, such as complying with constraints related to the system and business, and these constraints may vary from implementation to implementation. Furthermore, it should be understood that while development work can be very complex and time-consuming, such development work is merely a routine task for those skilled in the art who benefit from the present disclosure.

[0018] It should be understood that while the terms "first," "second," etc., may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, without departing from the scope of this disclosure, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element.

[0019] It should also be noted that, in order to avoid obscuring this disclosure with unnecessary details, only the equipment structure and / or processing steps closely related to the solution according to this disclosure are shown in the accompanying drawings, while other details that are not closely related to this disclosure are omitted.

[0020] The embodiments according to this disclosure are described in detail below with reference to the accompanying drawings.

[0021] First, refer to Figure 1 An implementation example of an information processing method for a flow cytometer according to embodiments of the present disclosure is described. Figure 1This is a flowchart illustrating an example of an information processing method 100 for a flow cytometer according to an embodiment of the present disclosure.

[0022] like Figure 1 As shown, the information processing method 100 according to an embodiment of the present disclosure may begin at a start step S102 and end at an end step S110. The information processing method 100 according to an embodiment of the present disclosure may include a detection signal acquisition step S104, an adjustment matrix calculation step S106, and a signal adjustment step S108.

[0023] In the signal acquisition step S104, the target sample can be detected by flow cytometry to obtain multiple target detection signals. For example, the multiple target detection signals can each correspond to multiple events. Each event can correspond to a unit (e.g., cell, particle, etc.) in the target sample. For example, the target sample can include multi-stained samples, but is not limited to this; rather, an appropriate target sample can be selected according to actual needs.

[0024] In the adjustment matrix calculation step S106, the adjustment matrix can be calculated as follows: based on the first component corresponding to the first detector of two different detectors among the multiple detectors included in the flow cytometer and the second component corresponding to the second detector of the two different detectors, the multiple target detection signals are divided into a first subset and a second subset; based on the first component and the second component of each target detection signal in the first subset, the first element of the two elements corresponding to the two different detectors in the adjustment matrix is ​​obtained; and based on the first component and the second component of each target detection signal in the second subset, the second element of the two elements corresponding to the two different detectors in the adjustment matrix is ​​obtained.

[0025] Note that the expression "two different detectors" in this article is not limited to two specific detectors, but can refer to a combination of one or more "two different detectors" corresponding to one or more elements in the adjustment matrix that need to be acquired. The specific meaning of this expression is detailed below. Figures 2 to 4 The described example will help to understand it better.

[0026] For example, the first subset can represent the target detection signal corresponding to a unit in the target sample where the label abundance of that unit corresponding to the first detector is greater than the label abundance corresponding to the second detector. Correspondingly, the second subset can represent the target detection signal corresponding to a unit in the target sample where the label abundance of that unit corresponding to the first detector is less than the label abundance corresponding to the second detector.

[0027] In the signal adjustment step S108, multiple target detection signals can be adjusted based on the adjustment matrix calculated in the adjustment matrix calculation step S106 to obtain an adjusted target detection signal.

[0028] As an example, in the detection signal acquisition step S104, a compensation matrix can be used to compensate for multiple original detection signals obtained by detecting the target sample by flow cytometry, so as to obtain multiple compensated detection signals as multiple target detection signals. For example, the compensation matrix can be a compensation matrix obtained from a single stained sample using existing methods (see, for example, Novo D, Grégori G, Rajwa B. Generalized unmixing model for multispectral flow cytometry utilizing nonsquare compensation matrices[J].Cytometry Part A,2013,83(5):508-520).

[0029] As mentioned earlier, in traditional flow cytometry, a compensation process is required to eliminate signal interference between different detection channels to obtain the final detection signal used to determine the tag abundance of each unit in the sample. However, the compensation matrix obtained from a single-stained sample is often imperfect. Therefore, further adjustments are needed to the detection signal after compensation based on the compensation matrix. In existing technology, this adjustment process is performed manually in the display interface of the detection signal. Specifically, the user manually adjusts the overflow parameter by dragging a slider, and ends the adjustment process if the user perceives the clusters in the scatter plot to appear "straight".

[0030] As described above, the information processing method 100 according to embodiments of the present disclosure can calculate an adjustment matrix and further adjust the compensated detection signal based on the adjustment matrix, thereby eliminating the need for manual adjustment by the user and improving convenience. Furthermore, since the adjustment process in the prior art depends on the user's intuitive perception (i.e., whether the cluster is straight), the results obtained by different users lack comparability, and the adjusted detection signal may not accurately represent the label abundance of each unit in the target sample. On the other hand, the information processing method 100 according to embodiments of the present disclosure does not depend on the user's intuitive perception, therefore the results obtained by different users can be compared with each other. Additionally, the information processing method 100 according to embodiments of the present disclosure can improve the accuracy of the label abundance of each unit in the target sample represented by the adjusted detection signal.

[0031] For example, the adjustment matrix calculation step S106 and the signal adjustment step S108 can be automatically executed in response to the acquisition of multiple target detection signals in the detection signal acquisition step S104, thereby further improving convenience and significantly improving data processing efficiency.

[0032] As an example, the multiple target detection signals acquired in the detection signal acquisition step S104 are obtained by detecting the target sample using flow cytometry to obtain multiple raw detection signals. That is to say, the compensation process based on the compensation matrix of the prior art can be omitted. In the signal adjustment step S108, the multiple raw detection signals can be adjusted using the adjustment matrix to obtain the final detection result.

[0033] The information processing method 100 according to embodiments of the present disclosure will be further described below with reference to specific examples.

[0034] For example, multiple target detection signals obtained by detecting m events using a flow cytometer with n detectors can be represented by a matrix R. m×n Representation. Matrix R m×n A single row in matrix R can represent a target detection signal corresponding to an event. m×n A single column in the array can represent the signal components of multiple target detection signals obtained through the same detector. For example, Figure 2 The diagram shows the signal component R obtained by the i-th detector based on multiple target detection signals. :,i and the signal component R obtained by the j-th detector :,j An example of a logarithmic scatter plot. In practical use cases, users tend to view logarithmic scatter plots. However, considering that scatter plots based on linear axes are more convenient to process, [the following can be used instead]. Figure 2 The logarithmic scatter plot shown is converted to Figure 3 The linear scatter plot shown.

[0035] exist Figure 2 and Figure 3 In this diagram, each data point corresponds to a target detection signal. Specifically, the x-coordinate of data point k is... k and the vertical coordinate y k These can be represented by the signal components R of the corresponding target detection signal obtained by the i-th detector. k,i The signal component R obtained by the j-th detector k,j .

[0036] For example, it can be Figure 3The scatter plot shown divides multiple data points into two subsets: the first subset and the second subset. As an example, a Gaussian mixture model can be used for subset partitioning. Specifically, two Gaussian models are set up: φ(x,y|θ1) and φ(x,y|θ2), where θ1=(μ1,Σ1), θ2=(μ2,Σ2) (see, for example, https: / / blog.csdn.net / jinping_shi / article / details / 59613054). Assume α... 1k and α 2k These are the data points (x) k ,y k The probability that α belongs to models φ(x,y|θ1) and φ(x,y|θ2), where α 1k +α 2k =1. The log-likelihood function can be expressed by the following equation (1):

[0037]

[0038] For example, the Expectation-Maximization (EM) method can be used to maximize logL(θ) in equation (1), and based on this, multiple data points can be divided into two subsets, for example... Figure 3 The first subset shown in the upper left corner and the second subset in the lower right The adjustment matrix S′ can be obtained based on the first subset. n×n The two elements s in the i-th detector and j-th detector are... j ′ i and s i ′ j The first element s in j ′ i And the second element s can be obtained based on the second subset. i ′ j For example, adjusting the elements s on the diagonal of the matrix. i ′ i It can be set to 1.

[0039] For example, the first subset can be The approximate slope relative to the y-axis is calculated as the first element s. j ′ i Specifically, the first component of each target detection signal in the first subset can be... The first component of the target detection signal in the first subset is used as the independent variable. Second component Perform a linear fit, and then use the resulting slope k 1As the first element s′ ji Similarly, the second subset can be... The approximate slope relative to the x-axis is calculated as the second element s′. ij Specifically, the second component of each target detection signal in the second subset can be... The first component Y of the target detection signals in the second subset is used as the independent variable. 2 Second component Perform a linear fit, and then use the resulting slope k 2 As the second element s′ ij By changing the specific values ​​of i and j, the adjustment matrix S′ can be obtained. n×n The element that needs to be retrieved.

[0040] The adjusted target detection signal obtained through signal adjustment step S108 can be represented as R. m×n (S′ n×n ) -1 .

[0041] In utilizing the compensation matrix S n×n For multiple raw detection signals R m×n Multiple compensated detection signals obtained through compensation. In the case of multiple target detection signals, the adjusted target detection signal obtained through signal adjustment step S108 can be expressed as follows:

[0042] For example, Figure 4 This shows a logarithmic scatter plot drawn based on the signal components corresponding to the i-th detector and the j-th detector of the adjusted target detection signal obtained through signal adjustment step S108. (Comparison) Figure 2 and Figure 4 As can be seen, the adjusted target detection signal effectively eliminates signal overflow between the detection channels corresponding to the i-th and j-th detectors.

[0043] As an example, before performing linear fitting, the first and second subsets can be processed using the random sample Consensus (RANSAC) algorithm to remove noise, thereby further improving the accuracy of the label abundance of each unit in the target sample represented by the adjusted detection signal.

[0044] Note that although this paper describes the use of Gaussian mixture models to subset the multiple target detection signals, techniques such as k-means and Bayesian methods can also be used for subset partitioning.

[0045] The above description has described an information processing method 100 for a flow cytometer according to embodiments of the present disclosure. Corresponding to the embodiments of the information processing method 100 described above, the present disclosure also provides the following embodiments of an information processing system for a flow cytometer. Figure 5 This is a block diagram illustrating a configuration example of an information processing system 500 for a flow cytometer according to an embodiment of the present disclosure.

[0046] For example, such as Figure 5 As shown, the information processing system 500 according to an embodiment of the present disclosure may include a detection signal acquisition unit 504, an adjustment matrix calculation unit 506, and a detection signal adjustment unit 508.

[0047] The detection signal acquisition unit 504 can be configured to detect a target sample using a flow cytometer to obtain multiple target detection signals. For example, the target sample may include a multi-stained sample. For example, the detection signal acquisition unit 504 can perform the detection signal acquisition step S104 described above; therefore, for details, please refer to the description of the detection signal acquisition step S104 above. Only a brief description will be provided below.

[0048] The adjustment matrix calculation unit 506 can be configured to calculate the adjustment matrix in the following manner: based on the first component corresponding to the first detector of two different detectors included in the flow cytometer and the second component corresponding to the second detector of the two different detectors, the multiple target detection signals are divided into a first subset and a second subset; based on the first component and the second component of each target detection signal in the first subset, the first element of the two elements corresponding to the two different detectors in the adjustment matrix is ​​obtained; and based on the first component and the second component of each target detection signal in the second subset, the second element of the two elements corresponding to the two different detectors in the adjustment matrix is ​​obtained. For example, the adjustment matrix calculation unit 506 can perform the adjustment matrix calculation step S106 described above, so for details, please refer to the description of the adjustment matrix calculation step S106 above. Only a brief description will be given below.

[0049] The detection signal adjustment unit 508 can be configured to adjust multiple target detection signals based on the adjustment matrix calculated by the adjustment matrix calculation unit 506 to obtain an adjusted target detection signal. For example, the detection signal adjustment unit 508 can execute the signal adjustment step S108 described above, so please refer to the description of the signal adjustment step S108 above for details. It will only be briefly described below.

[0050] As an example, the detection signal acquisition unit 504 can use a compensation matrix to compensate for multiple original detection signals obtained by detecting the target sample through flow cytometry, so as to obtain multiple compensated detection signals as multiple target detection signals.

[0051] Similar to the information processing method 100 described above, the information processing system 500 according to embodiments of this disclosure can eliminate the need for manual adjustments by the user, improving convenience and data processing efficiency. The adjustment process of the information processing system 500 according to embodiments of this disclosure does not depend on the user's intuitive perception; therefore, results obtained by different users can be compared. Furthermore, it can improve the accuracy of label abundance of each unit in the target sample represented by the adjusted detection signal.

[0052] As an example, the detection signal acquisition unit 504 can acquire multiple raw detection signals obtained by detecting the target sample through flow cytometry as multiple target detection signals.

[0053] For example, the adjustment matrix calculation unit 506 can use the first component of each target detection signal in the first subset as the independent variable, perform linear fitting on the first and second components of each target detection signal in the first subset, and use the resulting slope as the first element. Similarly, the adjustment matrix calculation unit 506 can use the second component of each target detection signal in the second subset as the independent variable, perform linear fitting on the first and second components of each target detection signal in the second subset, and use the resulting slope as the second element.

[0054] Furthermore, according to embodiments of this disclosure, a flow cytometer including the aforementioned information processing system 500 can also be provided. For example, the flow cytometer may include a spectral flow cytometer, but is not limited thereto.

[0055] It should be noted that although the above description of the information processing system and method for flow cytometer according to embodiments of the present disclosure, as well as the functional configuration and operation of the flow cytometer, is merely an example and not a limitation. Those skilled in the art can modify the above embodiments based on the principles of the present disclosure, such as adding, deleting, or combining functional modules and operations in various embodiments, and all such modifications fall within the scope of the present disclosure.

[0056] Furthermore, it should be noted that the system embodiments described here correspond to the method embodiments described above. Therefore, any content not described in detail in the system embodiments can be found in the description of the corresponding parts in the method embodiments, and will not be repeated here.

[0057] Furthermore, this disclosure also provides a storage medium and a program product. It should be understood that the machine-executable instructions in the storage medium and program product according to embodiments of this disclosure can also be configured to perform the aforementioned information processing methods; therefore, details not described in detail here can be referred to the descriptions in the preceding corresponding sections and will not be repeated here.

[0058] Accordingly, the storage medium used to carry the aforementioned program product including machine-executable instructions is also included in the disclosure of this invention. This storage medium includes, but is not limited to, floppy disks, optical disks, magneto-optical disks, memory cards, memory sticks, etc.

[0059] Furthermore, it should be noted that the aforementioned series of processes and systems can also be implemented via software and / or firmware. In the case of software and / or firmware implementation, data can be transferred from storage media or networks to computers with dedicated hardware architectures, such as… Figure 6 The general-purpose personal computer 1000 shown is equipped with the programs that constitute the software, and when various programs are installed, the computer is able to perform various functions, etc.

[0060] exist Figure 6 In this system, the central processing unit (CPU) 1001 performs various processes based on the program stored in the read-only memory (ROM) 1002 or the program loaded into the random access memory (RAM) 1003 from the storage section 1008. The RAM 1003 also stores, as needed, the data required when the CPU 1001 performs various processes.

[0061] CPU 1001, ROM 1002 and RAM 1003 are connected to each other via bus 1004. Input / output interface 1005 is also connected to bus 1004.

[0062] The following components are connected to the input / output interface 1005: input section 1006, including a keyboard, mouse, etc.; output section 1007, including a display, such as a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; storage section 1008, including a hard disk, etc.; and communication section 1009, including a network interface card, such as a LAN card, modem, etc. The communication section 1009 performs communication processing via a network, such as the Internet.

[0063] As needed, drive 1010 is also connected to input / output interface 1005. Removable media 1011, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on drive 1010 as needed, so that computer programs read from them can be installed into storage section 1008 as needed.

[0064] When the above series of processes are implemented through software, the program constituting the software is installed from a network such as the Internet or a storage medium such as removable media 1011.

[0065] Those skilled in the art will understand that such storage media are not limited to Figure 6 The illustration shows a removable medium 1011 containing a program, distributed separately from the device to provide the program to the user. Examples of removable media 1011 include magnetic disks (including floppy disks (registered trademark)), optical disks (including optical disc read-only memory (CD-ROM) and digital versatile disks (DVD)), magneto-optical disks (including mini-discs (MD) (registered trademark)), and semiconductor memory. Alternatively, the storage medium may be ROM 1002, a hard disk included in storage section 1008, etc., containing programs and distributed to the user along with the device containing them.

[0066] Preferred embodiments of the present disclosure have been described above with reference to the accompanying drawings, but the present disclosure is by no means limited to the examples described above. Various changes and modifications can be made by those skilled in the art within the scope of the appended claims, and it should be understood that such changes and modifications naturally fall within the technical scope of the present disclosure.

[0067] For example, the multiple functions included in one unit in the above embodiments can be implemented by separate devices. Alternatively, the multiple functions implemented by multiple units in the above embodiments can be implemented by separate devices respectively. In addition, one of the above functions can be implemented by multiple units. Needless to say, such a configuration is included within the scope of the present disclosure.

[0068] In this specification, the steps described in the flowchart include not only processes executed sequentially in the stated order, but also processes executed in parallel or individually, rather than necessarily sequentially. Furthermore, even within the steps of sequential processing, needless to say, the order can be appropriately altered.

Claims

1. An information processing method for flow cytometers, comprising: The target sample is detected by the flow cytometer to obtain multiple target detection signals; The adjustment matrix is ​​calculated as follows: Based on the first component corresponding to the first detector among two different detectors included in the flow cytometer, and the second component corresponding to the second detector among the two different detectors, the multiple target detection signals are divided into a first subset and a second subset. Based on the first and second components of the target detection signals in the first subset, the first element of the two elements corresponding to the two different detectors in the adjustment matrix is ​​obtained, and Based on the first and second components of the target detection signals in the second subset, the second element of the two elements in the adjustment matrix corresponding to the two different detectors is obtained; as well as The plurality of target detection signals are adjusted based on the adjustment matrix to obtain adjusted target detection signals. Among them, obtaining multiple target detection signals includes: The multiple original detection signals obtained by detecting the target sample through the flow cytometer are compensated using a compensation matrix to obtain multiple compensated detection signals as the multiple target detection signals.

2. The information processing method according to claim 1, wherein, Obtaining the first element includes: linearly fitting the first and second components of each target detection signal in the first subset to the first component as the independent variable, and using the resulting slope as the first element; and Obtaining the second element includes: using the second component of each target detection signal in the second subset as the independent variable, performing a linear fit on the first and second components of each target detection signal in the second subset, and using the slope obtained therefrom as the second element.

3. The information processing method according to claim 1 or 2, wherein, The multiple target detection signals are divided into a first subset and a second subset using a Gaussian mixture model.

4. The information processing method according to claim 2 further includes: Before performing linear fitting, the first subset and the second subset are processed using a random consistency sampling algorithm to remove noise.

5. The information processing method according to claim 1 or 2, wherein, The target samples include multi-stained samples.

6. An information processing system for a flow cytometer, comprising: The detection signal acquisition unit is configured to detect the target sample using the flow cytometer to obtain multiple target detection signals; The adjustment matrix calculation unit is configured to calculate the adjustment matrix in the following manner: Based on the first component corresponding to the first detector among two different detectors included in the flow cytometer, and the second component corresponding to the second detector among the two different detectors, the multiple target detection signals are divided into a first subset and a second subset. Based on the first and second components of the target detection signals in the first subset, the first element of the two elements corresponding to the two different detectors in the adjustment matrix is ​​obtained, and Based on the first and second components of the target detection signals in the second subset, the second element of the two elements in the adjustment matrix corresponding to the two different detectors is obtained; as well as The detection signal adjustment unit is configured to adjust the plurality of target detection signals based on the adjustment matrix to obtain an adjusted target detection signal. The detection signal acquisition unit is configured to use a compensation matrix to compensate multiple original detection signals obtained by detecting the target sample through the flow cytometer, so as to obtain multiple compensated detection signals as the multiple target detection signals.

7. The information processing system according to claim 6, wherein, Obtaining the first element includes: linearly fitting the first and second components of each target detection signal in the first subset to the first component as the independent variable, and using the resulting slope as the first element; and Obtaining the second element includes: using the second component of each target detection signal in the second subset as the independent variable, performing a linear fit on the first and second components of each target detection signal in the second subset, and using the slope obtained therefrom as the second element.

8. The information processing system according to claim 7, wherein, The adjustment matrix calculation unit is configured to divide the plurality of target detection signals into a first subset and a second subset using a Gaussian mixture model.

9. The information processing system according to claim 8, wherein, The adjustment matrix calculation unit is configured to process the first subset and the second subset using a random consistency sampling algorithm before performing linear fitting, in order to remove noise.

10. The information processing system according to claim 6 or 7, wherein, The target samples include multi-stained samples.

11. A flow cytometer comprising an information processing system according to any one of claims 6 to 10.

12. A computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the information processing method according to any one of claims 1 to 5.

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